Embodiments described herein provide for a fraud detection engine for detecting various types of fraud at a call center and a fraud importance engine for tailoring the fraud detection operations to relative importance of fraud events. Fraud importance engine determines which fraud events are comparative more important than others. The fraud detection engine comprises machine-learning models that consume contact data and fraud importance information for various anti-fraud processes. The fraud importance engine calculates importance scores for fraud events based on user-customized attributes, such as fraud-type or fraud activity. The fraud importance scores are used in various processes, such as model training, model selection, and selecting weights or hyper-parameters for the ML models, among others. The fraud detection engine uses the importance scores to prioritize fraud alerts for review. The fraud importance engine receives detection feedback, which contacts involved false negatives, where fraud events were undetected but should have been detected.
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2. The method according to claim 1, further comprising training, by the computer, the machine-learning model to determine the risk score corresponding to a particular type of fraud of an attribute value, by applying the machine-learning model on the contact data of one or more prior contacts and the fraud importance score of each fraud event in the one or more prior contacts.
8. The method according to claim 1, wherein the one or more attributes include at least one of: the type of fraud, a fraud activity, a cross-channel fraud activity, a penetration level associated with authentication, account information, fraudster identity, and spoofing information.
10. The method according to claim 1, further comprising transmitting, by the computer, a fraud alert notification containing the fraud alert in the fraud alert queue to a client computer according to the fraud importance score.
18. The system according to claim 11, wherein the one or more attributes include at least one of: the type of fraud, a fraud activity, a cross-channel fraud activity, a penetration level associated with authentication, account information, fraudster identity, and spoofing information.
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July 1, 2021
February 6, 2024
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